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Multi-Document Summarization

Multi-Document Summarization is a process of representing a set of documents with a short piece of text by capturing the relevant information and filtering out the redundant information. Two prominent approaches to Multi-Document Summarization are extractive and abstractive summarization. Extractive summarization systems aim to extract salient snippets, sentences or passages from documents, while abstractive summarization systems aim to concisely paraphrase the content of the documents.

Source: Multi-Document Summarization using Distributed Bag-of-Words Model

Papers

Showing 91100 of 359 papers

TitleStatusHype
Monolingual vs multilingual BERTology for Vietnamese extractive multi-document summarization0
MSˆ2: Multi-Document Summarization of Medical StudiesCode1
SgSum:Transforming Multi-document Summarization into Sub-graph SelectionCode0
SgSum: Transforming Multi-document Summarization into Sub-graph Selection0
Topic-Guided Abstractive Multi-Document Summarization0
3M:Multi-document Summarization Considering Main and Minor Relationship0
PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document SummarizationCode1
Modeling Endorsement for Multi-Document Abstractive SummarizationCode0
HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive SummarizationCode1
Extending Multi-Text Sentence Fusion Resources via Pyramid AnnotationsCode0
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